Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Neural Computation
Kernel PCA for similarity invariant shape recognition
Neurocomputing
Robust Identity Verification Based on Infrared Face Images
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Thermal Faceprint: A New Thermal Face Signature Extraction for Infrared Face Recognition
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Face recognition using non-linear image reconstruction
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
IR and visible light face recognition
Computer Vision and Image Understanding
Thermal face recognition in an operational scenario
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for multibiometrics
Expert Systems with Applications: An International Journal
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We present a low resolution face recognition technique based on a special type of convolutional neural network which is trained to extract facial features from face images and project them onto a low-dimensional space. The network is trained to reconstruct a reference image chosen beforehand, and it has been applied in visible and infrared light. Since the learning phase is achieved separately for the two modalities, the projections, and then the new spaces, are uncorrelated for the two networks. However, by normalizing the results of these two non-linear approaches, we can merge them according to a measure of saliency computed dynamically. We experimentally show that our approach obtain good results in terms of precision and robustness, especially on new and unseen subjects.